Roundoff errors in block-floating-point systems

Kalliojarvi, K.; Astola, Jaakko

doi:10.1109/78.492531

Cited by 69 publications

(20 citation statements)

References 15 publications

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“…Moreover, the amount of data simultaneously being transferred between each stage of the system would be unmanageable by standard CPUbased embedded systems. The bandpass filter bank contains multiple stages of IIR filters, heavily parallelized and making extensive use of block-floating-point arithmetic [44]. As such narrowband filters require a precise numerical representation in order to be stable, single-precision floating-point format was used on the implementation of the filter bank.…”

Section: Hardware Prototypementioning

confidence: 99%

Design of a Compact Sound Localization Device on a Stand-Alone FPGA-Based Platform

Kugler

Tossavainen

Kuroyanagi

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYSound localization systems are widely studied and have several potential applications, including hearing aid devices, surveillance and robotics. However, few proposed solutions target portable systems, such as wearable devices, which require a small unnoticeable platform, or unmanned aerial vehicles, in which weight and low power consumption are critical aspects. The main objective of this research is to achieve real-time sound localization capability in a small, self-contained device, without having to rely on large shaped platforms or complex microphone arrays. The proposed device has two surface-mount microphones spaced only 20 mm apart. Such reduced dimensions present challenges for the implementation, as differences in level and spectra become negligible, and only timedifference of arrival (TDoA) can be used as a localization cue. Three main issues have to be addressed in order to accomplish these objectives. To achieve real-time processing, the TDoA is calculated using zero-crossing spikes applied to the hardware-friendly Jeffers model. In order to make up for the reduction in resolution due to the small dimensions, the signal is upsampled several-fold within the system. Finally, a coherence-based spectral masking is used to select only frequency components with relevant TDoA information. The proposed system was implemented on a fieldprogrammable gate array (FPGA) based platform, due to the large amount of concurrent and independent tasks, which can be efficiently parallelized in reconfigurable hardware devices. Experimental results with white-noise and environmental sounds show high accuracies for both anechoic and reverberant conditions.

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Section: Hardware Prototypementioning

confidence: 99%

Design of a Compact Sound Localization Device on a Stand-Alone FPGA-Based Platform

Kugler

Tossavainen

Kuroyanagi

et al. 2016

IEICE Trans. Inf. & Syst.

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“…Transform domain compression algorithms include FFT-BAQ [2], WHTBAQ [3], DCT-BAQ, wavelet transform (WT) and compressed sensing Scalar compression algorithms include block adaptive quantization (BAQ) [4] block floating point quantization (BFPQ), fuzzy BAQ (FBAQ), entropyconstrained BAQ (ECBAQ) [5], and flexible block adaptive quantization (FBAQ). Vector compression algorithms involve vector quantization (VQ) [6], block adaptive vector quantization (BAVQ), block gain adaptive vector quantization (BGAVQ) [7], and trellis coded vector quantization (TCVQ) [8].…”

Section: Introductionmentioning

confidence: 99%

Vector Quantization for Satellite Image Compression

Joseph

2016

JCTECS

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Abstract-Image compression is the process of reducing the size of a file without humiliating the quality of the image to an unacceptable level by Human Visual System. The reduction in file size allows to store large amount of data in minimum memory, which can also speed up the transmission process in low bandwidth network, in the case of remote sensing the time consumption of image transmission from satellite to ground station will be reduced. This paper presents a coding scheme for satellite images using Vector Quantization which is a well-known technique for signal compression, and it is also the generalization of the scalar quantization. The given satellite image is compressed using VCDemo software by creating codebooks for vector quantization and the quality of the compressed and original image is compared by the Mean Square Error, Signal to Noise Ratio, Peak Signal to Noise Ratio values.

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“…A single exponent is assigned to a group of values to reduce memory requirements and arithmetic complexity. However, output signal quality depends on the block size and characteristics of the input signal [6]. Finding a common exponent requires processing of the complete block.…”

Section: B Bfpmentioning

confidence: 99%